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Infer.NET is a .NET framework for machine learning. It provides state-of-the-art message-passing algorithms and statistical routines for performing Bayesian inference. It has applications in a wide variety of domains, including information retrieval, bioinformatics, epidemiology, vision, and many others.

Infer.NET is a framework for running Bayesian inference in graphical models. You can use it to solve many different kinds of machine learning problems, from standard problems like classification or clustering through to customised solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.

The computer vision group in Cambridge is interested in the following areas of research: image and video editing, advanced medical image analysis, object class recognition, real-time stereo vision, remote collaboration, and visual tracking.

This is a compiler for the Bulk-Synchronous GPU Programming (BSGP) language. BSGP is a new language for general-purpose computation on a graphics processing unit (GPU). BSGP programs look similar to sequential C programs, and programmers need to supply only a bare minimum of extra information to describe parallel processing on GPUs. As a result, BSGP programs are easy to read, write, and maintain, and the ease of programming does not come at the cost of performance.

D* is a program for efficiently computing symbolic derivatives. This release includes source code tutorials showing basic D* usage, source code for Lagrangian dynamics, and source code for a simple geometric modeler. This version has a new linear time dynamics algorithm which is much faster than the algorithm used in previous releases, especially for systems with many degrees of freedom. The FAQ has been expanded with more details on techniques for optimizing performance.

D* is a language for conveniently expressing and computing ecient symbolic derivatives. There are many applications which require computing derivatives, and future chapters will describe several in great detail. This chapter will teach you how to write D* programs.
D* is implemented using a technique called language embedding. When you write a D* program you are actually programming in C#. Each type in the D* language has a corresponding C# class. D* mathematical operations are implemented by overloading the standard C# arithmetic operators and by providing special denitions for all the standard mathematical functions, such as sine and cosine.
D* code and C# code can be freely intermingled, with a few caveats. The most important is that the function Function.NewContext must be called before beginning the denition of any D* program. This sets up global data structures to keep track of all D* variable and function denitions.